K-Means算法分析与python实现

K-Means算法分析与python实现，CSDN博客地址：http://blog.csdn.net/gamer_gyt/article/details/48949227

scikit-learn下使用K-means：http://blog.csdn.net/gamer_gyt/article/details/51244850

Author: Thinkgamer

K-means/kMeans.py

@@ -0,0 +1,83 @@
+#coding:utf-8
+'''
+Created on 2015年11月9日
+
+@author: Administrator
+'''
+import numpy as np
+
+def kMeans(X, k, maxIt):
+    #横纵坐标
+    numPoints, numDim = X.shape
+    dataSet = np.zeros((numPoints, numDim + 1))
+    dataSet[: ,: -1 ] = X
+    #随机产生新的中心点
+    centroids = dataSet[np.random.randint(numPoints, size = k), :]
+    #centroids = dataSet[0:2,:]
+    #给k个中心点标签赋值为[1，k+1]
+    centroids[:, -1] = range(1, k+1)
+    
+    iterations = 0 #循环次数
+    oldCentroids = None  #用来储存旧的中心点
+    
+    while not shouldStop(oldCentroids, centroids, iterations, maxIt):
+        print "iterations:\n ",iterations
+        print "dataSet: \n",dataSet
+        print "centroids:\n ",centroids
+        
+        #用copy的原因是进行复制，不用=是因为=相当于同时指向一个地址，一个改变另外一个也会改变
+        oldCentroids = np.copy(centroids)
+        iterations += 1
+        
+        #更新中心点
+        updataLabels(dataSet, centroids)
+        
+        #得到新的中心点
+        centroids = getCentroids(dataSet, k)
+    
+    return dataSet
+    
+def shouldStop(oldCentroids, centroids, iterations, maxIt):
+    if iterations > maxIt:
+        return True
+    return np.array_equal(oldCentroids, centroids)
+
+
+def updataLabels(dataSet, centroids):
+    numPoints, numDim = dataSet.shape
+    for i in range(0,numPoints):
+        dataSet[i,-1] = getLabelFromCloseestCentroid(dataSet[i, :-1],centroids)
+        
+
+def getLabelFromCloseestCentroid(dataSetRow, centroids):
+    label = centroids[0, -1]
+    #np.linalg.norm() 计算两个向量之间的距离
+    minDist = np.linalg.norm(dataSetRow - centroids[0, :-1])
+    for i in range(1,centroids.shape[0]):
+        dist = np.linalg.norm(dataSetRow - centroids[i,:-1])
+        if dist < minDist:
+            minDist = dist
+            label = centroids[i,-1]
+            
+        print "minDist :\n" ,minDist
+        return label
+    
+    
+def getCentroids(dataSet, k):
+    result = np.zeros((k, dataSet.shape[1]))
+    for i in range(1, k+1):
+        oneCluster = dataSet[dataSet[:,-1] == i,:-1]
+        result[i - 1, :-1] = np.mean(oneCluster, axis=0)
+        result[i - 1, -1] = i
+        
+    return result
+
+
+x1 = np.array([1, 1])
+x2 = np.array([2, 1])
+x3 = np.array([4, 3])
+x4 = np.array([5, 4])
+testX = np.vstack((x1, x2, x3, x4))
+
+result = kMeans(testX, 2, 10)
+print "final result: \n",result